FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2898980648> ?p ?o ?g. }

• W2898980648 abstract "Article29 October 2018free access Source DataTransparent process Tumor suppressor PNRC1 blocks rRNA maturation by recruiting the decapping complex to the nucleolus Marco Gaviraghi orcid.org/0000-0003-2791-3638 Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Claudia Vivori Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Yerma Pareja Sanchez Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden Search for more papers by this author Francesca Invernizzi Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Angela Cattaneo Functional Proteomics Program, Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy Search for more papers by this author Benedetta Maria Santoliquido Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Michela Frenquelli Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Simona Segalla Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Angela Bachi Functional Proteomics Program, Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy Search for more papers by this author Claudio Doglioni Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Vicent Pelechano Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden Search for more papers by this author Davide Cittaro Center for Translational Genomics and Bioinformatics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Giovanni Tonon Corresponding Author [email protected] orcid.org/0000-0003-2973-5038 Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Center for Translational Genomics and Bioinformatics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Marco Gaviraghi orcid.org/0000-0003-2791-3638 Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Claudia Vivori Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Yerma Pareja Sanchez Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden Search for more papers by this author Francesca Invernizzi Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Angela Cattaneo Functional Proteomics Program, Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy Search for more papers by this author Benedetta Maria Santoliquido Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Michela Frenquelli Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Simona Segalla Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Angela Bachi Functional Proteomics Program, Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy Search for more papers by this author Claudio Doglioni Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Vicent Pelechano Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden Search for more papers by this author Davide Cittaro Center for Translational Genomics and Bioinformatics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Giovanni Tonon Corresponding Author [email protected] orcid.org/0000-0003-2973-5038 Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Center for Translational Genomics and Bioinformatics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy Search for more papers by this author Author Information Marco Gaviraghi1, Claudia Vivori1,†, Yerma Pareja Sanchez2, Francesca Invernizzi3, Angela Cattaneo4, Benedetta Maria Santoliquido1, Michela Frenquelli1, Simona Segalla1, Angela Bachi4, Claudio Doglioni3, Vicent Pelechano2, Davide Cittaro5 and Giovanni Tonon *,1,5 1Functional Genomics of Cancer Unit, Division of Experimental Oncology, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy 2Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Solna, Sweden 3Pathology Unit, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy 4Functional Proteomics Program, Istituto FIRC di Oncologia Molecolare (IFOM), Milan, Italy 5Center for Translational Genomics and Bioinformatics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute, Milan, Italy †Present address: Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain *Corresponding author. Tel: +39 0226435624; E-mail: [email protected] EMBO J (2018)37:e99179https://doi.org/10.15252/embj.201899179 See also: JS Mugridge & JD Gross (December 2018) PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Focal deletions occur frequently in the cancer genome. However, the putative tumor-suppressive genes residing within these regions have been difficult to pinpoint. To robustly identify these genes, we implemented a computational approach based on non-negative matrix factorization, NMF, and interrogated the TCGA dataset. This analysis revealed a metagene signature including a small subset of genes showing pervasive hemizygous deletions, reduced expression in cancer patient samples, and nucleolar function. Amid the genes belonging to this signature, we have identified PNRC1, a nuclear receptor coactivator. We found that PNRC1 interacts with the cytoplasmic DCP1α/DCP2 decapping machinery and hauls it inside the nucleolus. PNRC1-dependent nucleolar translocation of the decapping complex is associated with a decrease in the 5′-capped U3 and U8 snoRNA fractions, hampering ribosomal RNA maturation. As a result, PNRC1 ablates the enhanced proliferation triggered by established oncogenes such as RAS and MYC. These observations uncover a previously undescribed mechanism of tumor suppression, whereby the cytoplasmic decapping machinery is hauled within nucleoli, tightly regulating ribosomal RNA maturation. Synopsis Computational analysis of frequent cancer genome deletions reveals that PNRC1-dependent nucleolar recruitment of the cytoplasmic mRNA decapping complex blocks ribosomal maturation and ablates oncogene-induced cell proliferation. Nuclear receptor co-activator PNRC1 is frequently deleted in cancer cells. PNRC1 interacts with the DCP1α/DCP2 decapping complex and stimulates its translocation into the nucleolus. PNRC1 expression blocks ribosomal RNA processing in cancer cells. The nucleolar PNRC1-DCP1α/DCP2 complex targets the U3 and U8 snoRNAs for decapping. PNRC1 expression ablates oncogene-induced proliferation, suggesting a tumor suppressive role. Introduction The cancer genome is extensively rearranged, harboring somatic point mutations, chromosomal translocations as well as focal and large copy number alterations (CNAs). While there is overwhelming evidence that genes mutated or involved in chromosomal translocations exert crucial roles in carcinogenesis, the putative tumorigenic role of genes residing within gained or lost regions remains often elusive. In particular, identifying bona fine tumor suppressor genes (TSGs) within regions of chromosomal losses remains a daunting challenge. Building upon a survey that has collected genetic losses frequently present throughout various cancer types (Beroukhim et al, 2010), complementary approaches combining RNAi screens with sequencing-based information have led to the identification of novel TSGs (Nijhawan et al, 2012; Solimini et al, 2012). In particular, Solimini et al (2012) have revealed that within recurrent hemizygous focal deletions there is an enrichment of so-called STOP genes, which negatively affect proliferation. These studies have uncovered several unexpected TSGs that have been subsequently validated in depth (Solimini et al, 2013). However, a potential limitation of these approaches rests on the potential lack of cellular context (Goff, 2008; Mullenders & Bernards, 2009). Indeed, to vouch for consistency across experiments, oftentimes a single or, at most, a few reliable cell lines are used in these screens. As such, the appropriate biochemical and genetic environment might be missing, for cancer genes to unleash their oncogenic potential (Goff, 2008; Mullenders & Bernards, 2009). Furthermore, genes required for cancer cell survival in vivo are non-overlapping with those required in vitro (Miller et al, 2017). Hence, the exploitation of more extensive genomic data, including transcriptomic data, is warranted, for a more extensive and robust identification of potential TSGs. Nucleoli are highly dynamic structures where ribosomal RNA (rRNA) is synthesized and processed. rRNA originates from specific DNA sequences called nucleolar organizer regions (NORs) spread along the short arms of all human acrocentric chromosomes (Babu & Verma, 1985). NORs contain several copies of tandem-repeated sequences of ribosomal DNA genes (rDNA) (Sylvester et al, 2004), which are extensively transcribed by RNA polymerase I (PolI) as long polycistronic RNA molecules containing three of the four mature rRNA species, separated by spacer sequences (Cui & Tseng, 2004). These long rRNA precursors are then extensively processed to release the mature 28S, 18S, and 5.8S rRNA isoforms, reviewed in Mullineux and Lafontaine (2012). Several classes of enzymes participate to this complex series of reactions, including RNA helicases, endonucleases, and exonucleases belonging both to the 5–3′ or 3′–5′ RNA-degrading pathways (Mullineux & Lafontaine, 2012; Preti et al, 2013; Sloan et al, 2013). In addition, a class of non-coding nucleolar RNAs called small nucleolar RNAs (snoRNAs) exerts a crucial role in rRNA maturation, mainly driving the editing of rRNA precursor molecules (Matera et al, 2007; Kiss et al, 2010). Of note, a subgroup of 5′-capped snoRNAs, including U3 and U8, promotes specific cleavage steps of pre-rRNA molecules, directly influencing rRNA processing rates (Peculis & Steitz, 1993; Fayet-Lebaron et al, 2009; Perez-Fernandez et al, 2011). The size and number of nucleoli vary according to the rate of rRNA biosynthesis, which is carefully regulated according to various stimuli and stresses, and are frequently altered in different diseases (Boisvert et al, 2007) including cancer (Pianese, 1896). Indeed, aggressive tumors present hyperactivated rDNA transcription, which is required to boost ribosome biogenesis (Hein et al, 2013). Intriguingly, it is becoming evident that the increase in rRNA biogenesis triggered by oncogenic pathways is causally linked to cancer development (Barna et al, 2008; Chan et al, 2011). On the overall, the most established oncogenes, as, for example, MYC, RAS, and PI3K, strongly accelerate ribosome assembly by acting at the level of rRNA transcription and translation of ribosomal proteins (Boon et al, 2001; Arabi et al, 2003; Gomez-Roman et al, 2003; Schlosser et al, 2003; Zhao et al, 2003; Grandori et al, 2005; Mayer & Grummt, 2006; Drygin et al, 2010). While the general roles of oncogenic pathways in promoting rRNA transcription have been defined, the mechanistic wiring underlying rRNA maturation remains largely unknown. Within this frame, we have identified a novel tumor suppressor gene, the proline-rich nuclear receptor coactivator 1 (PNRC1), as a novel regulator of rRNA maturation. PNRC1 is formally labeled as a nuclear receptor (NR) co-activator as it interacts with several NRs and trans-activates exogenous NR reporter targets in a ligand-dependent manner (Zhou et al, 2000). In addition, PNRC1 has been proposed as a negative regulator of the cytoplasmic RAS signaling pathway (Zhou et al, 2004). A putative role for PNRC1 in promoting rRNA transcription was inferred from its nucleolar localization and its interaction with NPM1 nucleolar protein (Wang et al, 2011), as well as from its ability to interact with a subunit of RNA polymerase III (Zhou et al, 2007). PNRC1 also interacts with the RNA helicase UPF1, even though PNRC1 downregulation does not interfere with the RNA decay processes controlled by UPF1 (Cho et al, 2009). In this study, we show that PNRC1 re-expression in cancer cells interferes with rRNA processing through a novel molecular mechanism that relies on the nucleolar activity of the cytoplasmic DCP1α/DCP2 decapping complex, ultimately blocking oncogene-driven proliferation. Results Genes associated with the nucleolus are pervasively deleted and downregulated in several cancer types Seeking to identify novel TSGs, we combined RNA-seq with copy number alterations (CNAs) data derived from 28 TCGA cancers, for which both datasets were available. As in previous genetic screen efforts (Nijhawan et al, 2012; Solimini et al, 2012), we focused on 82 focal deletion peaks reported as frequently lost in cancers of various origins. These regions harbor a total of 2,060 genes (Beroukhim et al, 2010). We applied a novel iteration of non-negative matrix factorization (NMF) to extract tumor-specific signatures, subsequently analyzed to create a generalized set of signatures, to capture the mutual distribution of CNAs and variation of expression (Brunet et al, 2004; Carrasco et al, 2006; Fig EV1A). One signature stood out, whereby concomitant copy number loss and gene expression reduction were evident (Fig 1A). Unlike the other signatures, where residing genes presented also instances of increased expression, the 158 genes underlying this cluster were consistently downregulated, across several cancer types (Appendix Table S1). Moreover, these genes were included in regions of the genome presenting mostly hemizygous deletions. We next asked whether these genes presented any shared biological feature. Analysis of Cellular Compartment annotation revealed that the nucleolus was the GO term most significantly associated with this signature. Mindful that gene annotations are often outdated (Riba et al, 2016; Wadi et al, 2016), we sought evidences from the literature associating the nucleolus with the list of STOP genes included in this cluster. Of note, we found that among the genes belonging to this cluster, four were both STOP and implicated with the nucleolus, namely TP53, SMAD4, HMGN1, and PNRC1. Click here to expand this figure. Figure EV1. PNRC1 behaves as a tumor suppressor A. Visual representation of the NMF metagene signatures across multiple cancer types. The images represent the joint distribution of gene expression regulation (x-axis, z-score vs. normal tissue) and CNA (y-axis, exact segmentation value). Color intensity is proportional to the density value of the distribution. For each cluster, the number of assigned genes is reported in brackets. Cluster #3 represents the cluster enriched for genes concomitantly deleted and downregulated (as reported in Fig 1A). B. PNRC1 expression values in primary normal tissues (blue line), normal short-term cell lines (green line), and cancer cell lines (red line) retrieved from RefExA database (*HeLa, #MCF7). C, D. Immunofluorescence staining of PNRC1 (red) and Ki-67 (green) performed on primary samples belonging to normal lymphoid tissues. Cell nuclei were stained with DAPI. E. Proliferation curves of HeLa cells transfected with C-MYC (M), PNRC1 (P), their combination (MP), or a LacZ control (L). A representative experiment with the average ± SD of three technical replicates is shown. A one-way ANOVA with Tukey multiple comparison test was performed for 72-h dataset; the statistically significant comparisons are the following: L vs. M (P < 0.003), L vs. MP (P < 0.05), M vs. P (P < 0.0002), M vs. MP (P < 0.0001). Download figure Download PowerPoint Figure 1. PNRC1 tumor suppressor hinders oncogene-induced hyperproliferation A. Visual representation of the signature found associated with hemizygously deleted and downregulated genes across multiple cancer types. The image represents the joint distribution of gene expression regulation (x-axis, z-score vs. normal tissue) and copy number alteration (CNA) (y-axis, exact segmentation value). Color intensity is proportional to the density value of the distribution. B. Quantification of PNRC1 expression levels in primary normal (blue circles) and cancer (red circles) samples by real-time PCR. C, D. Immunofluorescence staining of PNRC1 (red) and Ki-67 (green) performed on a primary healthy colon (C) or a malignant lymphoma (D). DAPI was used to stain cell nuclei. Scale bars: 50 μm. E. Proliferation curves of HeLa cells transfected with HRASG12V (R), PNRC1 (P), their combination (RP), or a LacZ control (L). The results shown are the average ± SD of two biological replicates with three technical replicates each. A one-way ANOVA with Tukey multiple comparison test was performed for 72-h dataset; the statistically significant comparisons are: L vs. R (P < 0.002), L vs. P (P < 0.02), R vs. P (P < 0.0002), and R vs. RP (P < 0.0004). F. Proliferation curves of MCF7 cells transfected with HRASG12V (R), PNRC1 (P), their combination (RP), or a LacZ control (L). The results shown are the average ± SD of two biological replicates with three technical replicates each. A one-way ANOVA with Tukey multiple comparison test was performed for 72-h dataset; the statistically significant comparisons are: L vs. R (P < 0.003), L vs. P (P < 0.0002), L vs. RP (P < 0.0001), R vs. P (P < 0.0001), and R vs. RP (P < 0.0001). G. Proliferation curves of KRASG12S-mutated A549 cells transfected with PNRC1 or LacZ control. A representative experiment with the average ± SD of three technical replicates is shown. An unpaired t-test was performed for 144-h dataset (P < 0.0003). H. Proliferation curves of BJ-T cells transduced with a non-targeting (NT sh) or a PNRC1-specific shRNA construct (PNRC1 sh5). Results are shown as the average ± SD of three biological replicates with three technical replicates each. An unpaired t-test was performed for each time point (**P < 0.01, ***P < 0.005, ****P < 0.001). Download figure Download PowerPoint In all, these observations reveal that a subset of putative TSGs resides within regions of hemizygous deletions, is consistently downregulated throughout tumor samples and is enriched for features associated with nucleolar activities. PNRC1 loss is a frequent event in a wide variety of cancer types While the nucleolar and cancer roles for TP53, SMAD4, and HMGN1 are established, PNRC1 is deemed a nuclear receptor coactivator, with only one report suggesting a nucleolar localization (Wang et al, 2011). PNRC1 resides within a region that is pervasively lost in a large arrays of tumors, including prostate cancer (Verhagen et al, 2002; Lapointe et al, 2007; Boyd et al, 2012; Kluth et al, 2013), pancreatic cancer (Johansson et al, 1992), breast cancer (Poplawski et al, 2010), T-cell neoplasms (Remke et al, 2009; Lopez-Nieva et al, 2012), and hepatocellular carcinoma (Lee et al, 2008). We thus decided to explore in detail the potential role of PNRC1 as a putative tumor suppressor gene, in relationship with the nucleolus. We first substantiated the data emerging from the NMF analysis by analyzing PNRC1 expression levels in matched and unmatched tumor and normal samples. In line with the NMF analysis of TCGA data, PNRC1 expression was consistently lower in cancer compared to normal tissues (Figs 1B and EV1B). We also explored PNRC1 expression by immunofluorescence. Remarkably, both in cancer and in healthy tissues, the distribution of PNRC1 and the distribution of a proliferation marker, Ki-67, were steadily mutually exclusive (Figs 1C and D, and EV1C and D), suggesting that PNRC1 expression is a feature of non-proliferative cells. In all, these results suggest that PNRC1 might represent a novel tumor suppressor gene, based on its pervasive downregulation and localization within regions of hemizygous loss in cancer. PNRC1 thwarts RAS and MYC-driven proliferation As a first appraisal of the potential role of PNRC1 as a TSG, we tested whether PNRC1 could interfere with oncogene-induced enhanced proliferation in HeLa and MCF7 cell lines, which show low endogenous PNRC1 expression levels (Fig EV1B). The exogenous expression of PNRC1 by itself significantly reduced proliferation, when compared with the mock transfection, in HeLa (Fig 1E and Appendix Fig S1A) and MCF7 (Fig 1F and Appendix Fig S1B) cells in the absence of apoptosis (Fig EV2C), in line with a previous report (Zhou et al, 2004). To determine whether PNRC1 could impede oncogene-induced proliferation, we transfected PNRC1 alongside the HRASG12V oncogene. Remarkably, the increased proliferation following HRASG12V overexpression was completely ablated by the co-expression of PNRC1 in both cell lines (Fig 1E and F and Appendix Fig S2A). As a confirmation, PNRC1 expression reduced proliferation also of A549 cells, which endogenously express KRASG12S (Fig 1G and Appendix Fig S1C), as well as the anchorage-independent growth of HeLa (Fig EV2A) and A549 cells (Fig EV2B). We then explored whether the suppressive effect of PNRC1 on proliferation was more pervasive, extending also to other oncogenic pathways. We thus expressed PNRC1 alongside MYC in HeLa cells. As for HRASG12V overexpression, PNRC1 completely ablated MYC-induced proliferation (Fig EV1E and Appendix Fig S1D). To corroborate these results, we also silenced endogenous PNRC1 by shRNA. Whereas an almost complete PNRC1 silencing triggered apoptosis in HeLa cells (Fig EV2D and E, and Appendix Fig S2B), its mild downregulation resulted in an increased proliferative ability of immortalized BJ-T fibroblasts (Fig 1H and Appendix Fig S1E). Taken together, these data demonstrate that PNRC1 restrains the enhanced proliferation conferred by the expression of potent oncogenes and suggest that PNRC1 is endowed with tumor-suppressive activities. Click here to expand this figure. Figure EV2. PNRC1 effects on anchorage-independent growth and apoptosis A, B. Soft agar assays performed on HeLa (A) or A549 (B) cells expressing PNRC1 or GFP control. Images of representative wells, 10× widefield microscopy images of representative colonies, and the quantification of mean pixel intensities performed with ImageJ are reported. Quantifications are represented as averages ± SD of three technical replicates of a representative experiment. C. Annexin-V-PE/7-AAD flow cytometry dot plots of HeLa cells transfected with LacZ or PNRC1 transgenes. D. Annexin-V-PE/7-AAD flow cytometry dot plots of HeLa cells infected with a control shRNA or two specific PNRC1 shRNA constructs. E. Quantification of apoptotic HeLa cells infected with a control shRNA (Ctrl) or with PNRC1-specific shRNAs (sh4, sh5) according to their positivity to Annexin-V marker. Download figure Download PowerPoint PNRC1 affects nucleolar RNA dynamics Since from the TCGA data PNRC1 belongs to a signature characterized by the nucleolus GO term, we next explored whether PNRC1 had any broad activity in the regulation of nucleolar RNA dynamics. To this end, we initially performed an unbiased experiment taking advantage of the Click-iT pulse-chase approach. We exogenously expressed an RFP-tagged form of PNRC1 or an RFP control in HeLa cells (Appendix Fig S2C) and we pulsed cells with 5-ethynyl uridine (EU) for 16 h. Cells were then chased and the intensity and localization of neo-transcribed RNAs were followed by confocal microscopy. In line with a previous work (Wang et al, 2011), we could observe the accumulation of RFP-PNRC1 fusion protein in nuclear structures resembling nucleoli. Moreover, whereas control RFP-expressing cells showed an intense RNA signal inside nucleoli at earlier times, in RFP-PNRC1-transfected cells we could observe a dramatic reduction in the overall neotranscribed RNA, with a strong decrease in nascent RNA signal inside nucleoli (Fig 2A). In all, these results suggest that PNRC1 might negatively regulate rRNA biogenesis. Figure 2. PNRC1 interferes with nucleolar RNA metabolism A. Click-iT RNA imaging assay performed on HeLa cells expressing RFP-PNRC1 or RFP control. Confocal images of transfected cells pulsed with EU for 16 h were collected at the indicated time points after EU removal (green: Alexa 488-EU, red: RFP, blue: DAPI, scale bar: 5 μm). RFP-PNRC1-transfected cells are indicated with arrowheads. B, C. Confocal microscopy images of HeLa cells expressing GFP-PNRC1 and stained with antibodies against NPM1 (B) or UBF1 (C) nucleolar proteins (nuclei are stained in blue with DAPI, scale bar: 5 μm). Two representative cells are shown for each staining. A magnification of the merged channel images is provided for UBF1 staining. D. Agarose/formaldehyde gel separation of neotranscribed rRNAs collected from LacZ and PNRC1-expressing cells pulsed for 15 min with 3H-uridine and chased for the reported time points (minutes). Samples were loaded according to the amount of incorporated 3H, quantified by liquid scintillation. 5-fluorouridine (FU) was included as a control of a blocked rRNA processing. E. Ratios between the intensity of 47S pre-rRNA band and bands belonging to 32S or 28S rRNA species relative to panel (D) and measured at 60 min after the chase for both LacZ or PNRC1-expressing cells. F. Real-time PCR quantification of 5′-ETS steady-state levels in HeLa cells expressing PNRC1 or GFP control. The average ± SD of three biological replicates is shown. Statistical significance was calculated by an unpaired two-tailed t-test (P < 0.0001). Download figure Download PowerPoint We next aimed to determine the molecular role of PNRC1 in the repression of rRNA synthesis. According to the literature, rRNA transcription occurs between the inner nucleolar fibrillar centers (FC) and the surrounding dense fibrillar component (DFC), where rDNA is complexed with UBF1 transcription factor and RNA PolI machinery (Raska, 2003; Moss et al, 2007). Once transcribed, rRNA is processed in a contiguous sub-nucleolar compartment, the outer granular component (GC), defined by the presence of nucleophosmin (NPM1) protein (Boisvert et al, 2007; Ma & Pederson, 2008). To initially define whether PNRC1 participates in rRNA transcription or in rRNA processing, we aimed to define PNRC1 sub-nucleolar localization in relationship with UBF1 and NPM1 nucleolar markers, respectively. As reported (Wang et al, 2011), HA-PNRC1 co-precipitated with NPM1 (Fig EV3A). In line with this observation, our confocal microscopy experiments showed a strict co-localization between GFP-PNRC1 and NPM1 (Fig 2B). Notably, however, GFP-PNRC1 localization was mutually exclusive with UBF1 (Fig 2C), indicating that PNRC1 resides exclusively within the nucleolar GC. These data hence suggest that PNRC1 does not participate in rRNA transcription, as previously proposed (Wang et al, 2011), but instead regulates rRNA processing. Click here to expand this figure. Figure EV3. PNRC1 halts rRNA processing and recruits endogenous DCP1α inside nucleoli A. Co-immunoprecipitation performed on HeLa cells expressing HA-PNRC1 with a specific anti-HA antibody and probed with anti-NPM1 antibody. B. Quantification of ITS1 stea" @default.
• W2898980648 created "2018-11-09" @default.
• W2898980648 creator A5010457547 @default.
• W2898980648 creator A5018891781 @default.
• W2898980648 creator A5021816916 @default.
• W2898980648 creator A5030709418 @default.
• W2898980648 creator A5039853948 @default.
• W2898980648 creator A5044078690 @default.
• W2898980648 creator A5047380313 @default.
• W2898980648 creator A5050323670 @default.
• W2898980648 creator A5050499123 @default.
• W2898980648 creator A5052225450 @default.
• W2898980648 creator A5072118187 @default.
• W2898980648 creator A5072230824 @default.
• W2898980648 creator A5075384838 @default.
• W2898980648 date "2018-10-29" @default.
• W2898980648 modified "2023-10-10" @default.
• W2898980648 title "Tumor suppressor <scp>PNRC</scp> 1 blocks r <scp>RNA</scp> maturation by recruiting the decapping complex to the nucleolus" @default.
• W2898980648 cites W1660270104 @default.
• W2898980648 cites W1969673383 @default.
• W2898980648 cites W1969947723 @default.
• W2898980648 cites W1971284016 @default.
• W2898980648 cites W1972026067 @default.
• W2898980648 cites W1972087054 @default.
• W2898980648 cites W1972778166 @default.
• W2898980648 cites W1973633445 @default.
• W2898980648 cites W1976956469 @default.
• W2898980648 cites W1978120052 @default.
• W2898980648 cites W1986082240 @default.
• W2898980648 cites W198614898 @default.
• W2898980648 cites W1988079028 @default.
• W2898980648 cites W1991691204 @default.
• W2898980648 cites W1998929627 @default.
• W2898980648 cites W2002124740 @default.
• W2898980648 cites W2002373110 @default.
• W2898980648 cites W2002829809 @default.
• W2898980648 cites W2003911773 @default.
• W2898980648 cites W2004352744 @default.
• W2898980648 cites W2006978909 @default.
• W2898980648 cites W2009152490 @default.
• W2898980648 cites W2010116656 @default.
• W2898980648 cites W2013356610 @default.
• W2898980648 cites W2019791676 @default.
• W2898980648 cites W2022625015 @default.
• W2898980648 cites W2024459125 @default.
• W2898980648 cites W2024908788 @default.
• W2898980648 cites W2030473151 @default.
• W2898980648 cites W2036592951 @default.
• W2898980648 cites W2037235549 @default.
• W2898980648 cites W2039171339 @default.
• W2898980648 cites W2039183802 @default.
• W2898980648 cites W2041157094 @default.
• W2898980648 cites W2041236924 @default.
• W2898980648 cites W2043848225 @default.
• W2898980648 cites W2047282247 @default.
• W2898980648 cites W2047479529 @default.
• W2898980648 cites W2047682618 @default.
• W2898980648 cites W2053820532 @default.
• W2898980648 cites W2054544162 @default.
• W2898980648 cites W2064259285 @default.
• W2898980648 cites W2067058481 @default.
• W2898980648 cites W2070050178 @default.
• W2898980648 cites W2071491707 @default.
• W2898980648 cites W2074062134 @default.
• W2898980648 cites W2077858999 @default.
• W2898980648 cites W2080531393 @default.
• W2898980648 cites W2082343657 @default.
• W2898980648 cites W2083581022 @default.
• W2898980648 cites W2085938916 @default.
• W2898980648 cites W2090591978 @default.
• W2898980648 cites W2091430335 @default.
• W2898980648 cites W2091481108 @default.
• W2898980648 cites W2096455435 @default.
• W2898980648 cites W2096465161 @default.
• W2898980648 cites W2098066508 @default.
• W2898980648 cites W2099814921 @default.
• W2898980648 cites W2109872488 @default.
• W2898980648 cites W2110096996 @default.
• W2898980648 cites W2118853040 @default.
• W2898980648 cites W2122660019 @default.
• W2898980648 cites W2123282517 @default.
• W2898980648 cites W2129654938 @default.
• W2898980648 cites W2130070305 @default.
• W2898980648 cites W2136787567 @default.
• W2898980648 cites W2138885312 @default.
• W2898980648 cites W2140995557 @default.
• W2898980648 cites W2142571092 @default.
• W2898980648 cites W2148181651 @default.
• W2898980648 cites W2149441684 @default.
• W2898980648 cites W2151529426 @default.
• W2898980648 cites W2153056550 @default.
• W2898980648 cites W2154376431 @default.
• W2898980648 cites W2156988550 @default.
• W2898980648 cites W2166879817 @default.
• W2898980648 cites W2338705374 @default.
• W2898980648 cites W2396066281 @default.
• W2898980648 cites W2517051411 @default.
• W2898980648 cites W2592715362 @default.
• W2898980648 cites W2727119164 @default.
• W2898980648 cites W2792145634 @default.

• next